Methods, systems, and media for content ranking using real-time data

ABSTRACT

Methods, systems, and media for content ranking using real-time data are provided. In accordance with some embodiments of the present invention, a method, implemented on a processor, for ranking content is provided. The method can include, among other things: receiving real-time information from a plurality of sources; supplementing the received real-time information with historical information and user influence information; analyzing the supplemented real-time information from the plurality of sources to determine real-time trend information; receiving a plurality of content snippets from a content provider; detecting similarities between each of the plurality of content snippets and the determined real-time trend information; ranking the plurality of content snippets based on the detected similarities; and displaying the ranked plurality of content snippets.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 61/355,360, filed Jun. 16, 2010, which is herebyincorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The invention relates to ranking content using real-time data. Moreparticularly, methods, systems, and media for ranking content based onreal-time data, which can include real-time online trend data, influencedata, and/or user interest and behavior data, are provided.

BACKGROUND

Digital publishers and advertisers working through their contentmanagement systems are constantly searching for approaches to place theright content in front of the right user at the right time in order toincrease traffic to their websites.

Current approaches for presenting such content include using humaneditors that manually label and sort their content before it ispublished online. Once the content order is decided, it no longerchanges on a user and/or time basis. Nowadays, content managementsystems are offering simple heuristic systems for ranking content whichare based upon views or clicks (popular stories) or based upon views pertime period (trending stories). However, these approaches only promotewhat users have already found to be relevant (for example, relevant towhat is being talked about or related to a user's interests) or what thehuman editors have biased to succeed on the page. It should also benoted that there is a self-fulfilling false positive that minimizes theeffective user engagement on the website.

To further complicate this, the social media ecosystem is exploding withusers, usage, and innovation creating a real-time distributed mediaenvironment. Sharing of different types of content is happening acrossthe web and other digital environments through, for example, socialmedia networks (e.g., Facebook), Twitter, social content sharingservices (e.g., Digg), e-mail, instant messaging, short message service(SMS) messaging, and various web services (e.g., ShareThis, AddThis, andbit.ly), among others.

With the advent of social media, consumer behavior has changed such thatconversations and sharing of brands, content, and/or services ishappening across the real-time web and not only at the destination siteor through search engine results (e.g., using Google). As a result,publishers and advertisers are searching for approaches to leverage thislarge and growing source of traffic to their websites. Furthermore, asthis growth continues, social media, among other things, has increasedthe influence of individuals on the web. By following other users onTwitter or participating in social networks, users may influence oneanother when making decisions on content consumption.

There is therefore a need in the art for approaches for ranking contentusing real-time information. For example, these content managementsystems do not have a third party source of information that is unbiasedand feeding in the latest trends and topics being discussed on thereal-time web by users with similar interests and matching them to thepublishers distribution content.

Accordingly, it is desirable to provide methods, systems, and media thatovercome these and other deficiencies of the prior art.

SUMMARY OF THE INVENTION

Mechanisms for ranking content using real-time data are provided.

Generally speaking, these mechanisms collect real-time data frommultiple sources and rank content based on the real-time data. Moreparticularly, these mechanisms rank and/or re-sort digital contentbefore it is presented to a user by taking into account real-time data(e.g., real-time online trends, real-time influence data, a user's shortand long term interests and past behaviors, etc.).

These mechanisms, such as methods, systems, and media, can be used in avariety of applications. For example, digital publishers and advertisersworking through their content management systems are constantlysearching for approaches to place the right content in front of theright user at the right time in order to increase traffic to theirwebsites. Content of a publisher is ranked before presenting it to abrowser user while taking into account online trends, contentpopularity, user interests, etc. For example, content can be ranked atevery instance prior to presenting it to a browser such that the orderof ranked content changes at predefined intervals (e.g., every thirtyseconds, every minute, every ten seconds between the hours of 9 AM and 9PM and every minute in all other scenarios, etc.). These mechanisms canbe used for content optimization, where a particular publisher's contentcan be matched with real-time trend information. In a more particularexample, these mechanisms can be used to optimize and/or promoteparticular content to a position of discovery. This content discoveryapproach can help publishers and/or advertisers place their content infront of the user resulting in higher click through rates on their webpages, which translates to a substantial increase in subscriptionsand/or advertising revenue. In addition, this can also lower bouncerates on these pages and provide better search engine optimizations(SEOs) as substantially more relevant content based on real-timeinformation are promoted on their pages and spread through the network(sometimes referred to as high velocity stories or high velocitycontent).

Accordingly, these mechanisms can lead to more clicks, which leads tomore page views, thereby allowing entities, such as content providersand publishers, to significantly grow their user base, increase actionson their websites, and/or increase advertising revenue.

It should be noted that the nature of the explosive growth in usage andparticipation has created an active stream of unstructured data that canbe leveraged for creating rich actionable insights to core solutions,such as content optimization, automated publishing, and intelligentparticipation in the real-time web.

Methods, systems, and media for content ranking using real-time data areprovided. In accordance with some embodiments of the present invention,a method, implemented on a processor, for ranking content is provided.The method can include: receiving real-time information from a pluralityof sources; supplementing the received real-time information withhistorical information and user influence information; analyzing thesupplemented real-time information from the plurality of sources todetermine real-time trend information; receiving a plurality of contentsnippets from a content provider; detecting similarities between each ofthe plurality of content snippets and the determined real-time trendinformation; ranking the plurality of content snippets based on thedetected similarities; and displaying the ranked plurality of contentsnippets.

In some embodiments, the received real-time information includes atleast one of structured real-time information and unstructured real-timeinformation.

In some embodiments, the plurality of content snippets from a contentprovider comprises receiving the plurality of content snippets from adata feed. However, it should be noted that content or content packetsfrom one or more content providers can be received in any suitable dataformat. For example, in some embodiments, the data feed is one of: aReally Simple Syndication (RSS) feed, an Atom feed, an Extensible MarkupLanguage (XML) feed, and data provided in JavaScript Object Notation(JSON) format.

In some embodiments, the plurality of sources providing real-timeinformation comprise one or more of: a social media network, a socialmessaging service, a social content sharing service, an e-mail message,an instant message, a short message service (SMS) message, a webservice, a weblog, and a search engine.

In some embodiments, the user influence information includes personallyrelated information received or derived from a user. In someembodiments, the method further comprises ranking the plurality ofcontent snippets based on the detected similarities with the determinedreal-time trend information and based on the historical and personallyrelated information received or derived from the user.

In some embodiments, the method further comprises generating an index ofthe determined real-time trend information, where a plurality ofkeywords are extracted from the supplemented real-time information andan association between the plurality of keywords is created to generatethe index.

In some embodiments, the method further comprises detecting similaritiesbetween each of the plurality of content snippets and the determinedreal-time trend information by extracting at least one keyword from eachof the plurality of content snippets, calculating a similarity scorebased on a comparison of the extract keyword with each trend in thegenerated index, and aggregating the similarity score for each trend togenerate an aggregated similarity score, where the plurality of contentsnippets are ranked based at least in part on the aggregated similarityscore.

In some embodiments, the method further comprises: receiving a pluralityof data feeds from a plurality of web sources; retrieving contentsnippets from each of the plurality of data feeds; ranking the retrievedcontent snippets, wherein the ranking comprises: detecting similaritiesbetween each of the plurality of content snippets and real-time trendinformation from a trend index; generating a personal relevance scorefor each content snippet; and sorting each of the plurality of contentsnippets into a content listing based on the personal relevance score;and displaying the ranked content snippets.

In some embodiments, the method further comprises a feedback mechanismthat displays a list containing unranked content snippets, where thecontent snippets are the content snippets in the ranked contentsnippets; receiving feedback from a plurality of users with respect tothe list containing the unranked content snippets; and using thereceived feedback to determine performance of one or more models used torank the content snippets.

In some embodiments, a system for ranking content is provided, thesystem comprising a processor that: receives real-time information froma plurality of sources; supplements the received real-time informationwith historical information and user influence information; analyzes thesupplemented real-time information from the plurality of sources todetermine real-time trend information; receives a plurality of contentsnippets from a content provider; detects similarities between each ofthe plurality of content snippets and the determined real-time trendinformation; ranks the plurality of content snippets based on thedetected similarities; and displays the ranked plurality of contentsnippets.

In some embodiments, a non-transitory computer-readable mediumcontaining computer-executable instructions that, when executed by aprocessor, cause the processor to perform a method for ranking contentis provided, the method comprising: receiving real-time information froma plurality of sources; supplementing the received real-time informationwith historical information and user influence information; analyzingthe supplemented real-time information from the plurality of sources todetermine real-time trend information; receiving a plurality of contentsnippets from a content provider; detecting similarities between each ofthe plurality of content snippets and the determined real-time trendinformation; ranking the plurality of content snippets based on thedetected similarities; and displaying the ranked plurality of contentsnippets.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects, features, and advantages of the disclosed subjectmatter can be more fully appreciated with reference to the followingdetailed description of the invention when considered in connection withthe following drawing, in which like reference numerals identify likeelements.

FIG. 1 is a diagram of an illustrative content ranking application inaccordance with some embodiments of the disclosed subject matter.

FIG. 2 is a diagram of an illustrative content ranking application usedwith RSS feeds or other suitable content representations in accordancewith some embodiments of the disclosed subject matter.

FIG. 3 is a diagram of an illustrative content selection screendisplayed by the content ranking application in accordance with someembodiments of the disclosed subject matter.

FIG. 4 is a diagram of an illustrative content selection screendisplayed by the content ranking application in accordance with someembodiments of the disclosed subject matter.

FIGS. 5 and 6 are diagrams of illustrative ranked content screensdisplayed by the content ranking application in accordance with someembodiments of the disclosed subject matter.

FIG. 7 is a diagram of an illustrative user-inputted content selectionscreen displayed by the content ranking application in accordance withsome embodiments of the disclosed subject matter.

FIG. 8 is a diagram of an illustrative ranked content screen in responseto receiving a user-inputted feed that is displayed by the contentranking application in accordance with some embodiments of the disclosedsubject matter.

FIG. 9 is a diagram of an illustrative popular content screen that isdisplayed by the content ranking application in accordance with someembodiments of the disclosed subject matter.

FIG. 10 is a diagram of an illustrative widget creation screen displayedby the content ranking application in accordance with some embodimentsof the disclosed subject matter.

FIG. 11 is a diagram of an illustrative widget preview screen displayedby the content ranking application in accordance with some embodimentsof the disclosed subject matter.

FIG. 12 is a diagram of an illustrative system on which a ratingapplication can be implemented in accordance with some embodiments ofthe disclosed subject matter.

FIG. 13 is a diagram of an illustrative user computer and server asprovided, for example, in FIG. 12 in accordance with some embodiments ofthe disclosed subject matter.

DETAILED DESCRIPTION OF THE INVENTION

Methods, systems, and media for ranking content using real-time data areprovided.

Generally speaking, these mechanisms provide a content rankingapplication (sometimes referred to herein as “the application”). Moreparticularly, the application can use collected real-time data frommultiple sources to rank (or re-rank) content based on the real-timedata. The application can rank, re-rank, and/or re-sort digital contentbefore it is presented to a browser user by taking into accountreal-time data (e.g., real-time online trends, real-time influence data,a user's short and long term interests and past behaviors, etc.) fromone or more sources. For example, content can be ranked at everyinstance prior to presenting it to a browser such that the order ofranked content can change at predefined intervals (e.g., every thirtyseconds, every minute, every ten seconds between the hours of 9 AM and 9PM and every minute in other scenarios, etc.).

For example, as shown in FIG. 1, content ranking application 110 canreceive structured and/or unstructured real-time data from one or moresources 120. These sources 120 can include, for example, social medianetworks (e.g., Facebook, LinkedIn, MySpace etc.), social networkingservices (e.g., Twitter), social content sharing services (e.g., Digg),e-mail, instant messaging, short message service (SMS) messaging,various web services (e.g., ShareThis, AddThis, and bit.ly), weblogs,search engines (e.g., Google, Bing, Yahoo, etc.), and/or any othersuitable source of real-time data.

As also shown in FIG. 1, in some embodiments, content rankingapplication 110 can receive click feedback from one or more sources. Forexample, content ranking application 110 can receive a stream of clickdata based on user feedback or a click log. In another example, contentranking application 110 can receive a stream of click data relating to aparticular user that is viewing a particular web page such that thecontent ranking application 110 can rank and/or present content on thewebpage to that particular user. In yet another example, content rankingapplication 110 can aggregate or collect click feedback 130 foranalysis. Alternatively, as described herein, content rankingapplication 110 can create a feedback loop by monitoring and/orcapturing click data from one or more users on, for example, a rankedlist of content snippets. More particularly, the feedback loop can beused by the content ranking application 110 to compare the engagementwith a ranked list of content snippets and the engagement with anunranked list of content snippets. Click feedback and click redirectioncan be used to train, update, and/or optimize the models for matchingcontent to determined trend data, the models for ranking content, themodels for indexing real-time trend information, etc.

Using real-time data from one or more sources 120 (e.g., real-timeonline trends, real-time influence data, a user's short and long terminterests and past behaviors, etc.) and/or click feedback 130, thecontent ranking application 110 can obtain unranked content 140 (e.g.,content snippets from a particular web site) and rank the content toprovide ranked content 150.

These mechanisms, such as methods, systems, and media, can be used in avariety of applications. For example, digital publishers and advertisersworking through their content management systems are constantlysearching for approaches to place the right content in front of theright user at the right time in order to increase traffic to theirwebsites. Content of a publisher is ranked before presenting it to abrowser user while taking into account online trends, contentpopularity, user interests, etc. For example, content can be ranked atevery instance prior to presenting it to a browser such that the orderof ranked content changes at predefined intervals (e.g., every thirtyseconds, every minute, every ten seconds between the hours of 9 AM and 9PM and every minute in all other scenarios, etc.). These mechanisms canbe used for content optimization, where a particular publisher's contentcan be matched with real-time trend information. In a more particularexample, these mechanisms can be used to optimize and/or promoteparticular content to a position of discovery. This content discoveryapproach can help publishers and/or advertisers place their content infront of the user resulting in higher click through rates on their webpages, which translates to a substantial increase in subscriptionsand/or advertising revenue. In addition, this can also lower bouncerates on these pages and provide better search engine optimizations(SEOs) as substantially more relevant content based on real-timeinformation are promoted on their pages and spread through the network(sometimes referred to as high velocity stories or high velocitycontent).

Accordingly, these mechanisms can lead to more clicks, which leads tomore page views, thereby allowing entities, such as content providersand publishers, to significantly grow their user base, increase actionson their websites, and/or increase advertising revenue.

The nature of the explosive growth in usage and participation hascreated an active stream of unstructured data that can be leveraged forcreating rich actionable insights to core solutions, such as contentoptimization, automated publishing, and intelligent participation in thereal-time web.

A more particular example or implementation is shown in FIG. 2. Asshown, the application can include a data processing module 210 thatcollects structured and/or unstructured real-time data from multiplesources 220 (e.g., online data feed 1 through online data feed N). Asdescribed above, these sources can include, for example, social medianetworks (e.g., Facebook, LinkedIn, MySpace etc.), social networkingservices (e.g., Twitter), social content sharing services (e.g., Digg),e-mail, instant messaging, short message service (SMS) messaging,various web services (e.g., ShareThis, AddThis, and bit.ly), weblogs,search engines (e.g., Google, Bing, Yahoo, etc.), etc. Each of thesesources can provide real-time data, which can include, for example,real-time data queries, structured trend queries, influence dataqueries, sentiment queries, and/or any other suitable information. Inaddition, an association between data sources can be created throughusers, date time stamp, trends, and other suitable characteristics.

It should be noted that, although generally described in some of theembodiments herein that the application is receiving content snippetsfrom a RSS feed or an Atom feed, any suitable content or data packetsfrom a content provider can be received. For example, a data feed inJavaScript Object Notation (JSON) format can be received by theapplication. Moreover, multiple sources can provide content to theapplication for analysis and/or ranking. For example, the applicationcan receive multiple streams of data from multiple sources, where theapplication compares the content from each of the multiple sources toreal-time data and ranks the content into a single list.

In some embodiments, data processor 210 or any other suitable module ofthe application can cleanse the collected real-time data. For example,data processor 210 can analyze the real-time data for new entities,keywords, or elements to be indexed. In another example, data processor210 can clean the real-time data to remove extraneous data. In a moreparticular example, data processor 210 can be instructed to removecollected data that can identify the browser user (e.g., social securitynumbers, tax identification information, address information, creditcard numbers, maiden names, etc.), thereby ensuring that indexed trendsor other real-time information does not accidentally publish such dataor that such data does not become part of an index or taxonomy.

In some embodiments, data processor 210 or any other suitable module ofthe application can also supplement real-time data with additionalinformation. For example, data processor 210 can supplement real-timedata with historical data and user influence data or scores.

Users in the real-time web determine what is popular through theirparticipation and/or engagement, where more users discussing a contentsnippet and/or a topic increases its popularity and relevance in thestream. User influence data can be used to provide an indication thatallows real-time content to be differentiated based on the influencescore of the user. That is, data processor 210 or any other suitablemodule of the application can determine that there are one or moreinfluencing users based on the unstructured real-time data from multiplesources. In such an example, each user can be assigned an influencescore, where trend data, historical data, and other data relating to auser with a higher influence score can influence, among other things,approaches for ranking content. In addition, content snippets thatinclude the influencing user can be differentiated from other contentsnippets, where the influence score can be encoded or accounted forwithin a trend score.

Historical data can also be used to differentiate content—e.g., contentthat has high engagement or click rates can be differentiated fromcontent with low engagement or click rates. In a more particularexample, the historical response to data of the same category can beused as a real-time trend. That is, historical data can supplement trenddata to differentiate which trends or types of trends (e.g., names vs.products and activities) are more effective and/or the particulardemographic properties (e.g., time of the day, day of the week,geographic location, etc.) where each trend may be more effective. Forexample, the information that a user that frequents a particulargeographic location based on positioning information from a mobiledevice can be used to supplement real-time information when providingranked content to that user.

Any suitable piece of data can supplement real-time trend data andassist data processor 210 or any other suitable module of theapplication determine how to rank the content.

It should be noted that, in some embodiments, the process of collectingreal-time data and cleansing, refining, supplementing, and/or expandingit, can be performed at predetermined times (e.g., every 30 seconds,every few minutes, etc.) to keep up with a constantly changing real-timedistributed environment.

Referring back to FIG. 2, in response to collecting the real-time dataand cleansing, refining, supplementing, and/or expanding it, trend dataand/or user influence data can be indexed into trend index 230. Forexample, a real-time ranking module 240 of the application can determinereal-time online trends (e.g., the latest trends and topics) based onthe unstructured real-time data from multiple sources. In a moreparticular example, the real-time ranking module 240 or any othercomponent of the application can collect unstructured real-time datafrom multiple sources, create an association between the real-time datafrom the multiple sources through users, timestamps, trends, and/or anyother suitable characteristics, cleanse the real-time data by analyzingthe data for new entities, keywords, or other elements to be indexed andremoving extraneous data (e.g., personal user data), supplement thereal-time data with user influence data and/or historical data, andcreate the index.

It should be noted that the index can be modified, updated, refined,and/or expanded at any suitable time (e.g., every minute). For example,in response to continuously receiving real-time data and contentsnippets, the application can continue to determine new entities,trends, keywords, and/or other elements for indexing. The index cancontinue to grow and increase strength as additional information isprocessed and additional trends are determined. In another example,using historical information, the index can have the ability to trackseasonal or repeat events and account for this when ranking content.

Upon receiving processed trends and supplemental data from index 230 oranalyzing real-time data to determine real-time trends, real-timeranking module 240 can receive data packets from one or more contentproviders. For example, one or more really simple syndication (RSS)feeds 250 from a particular publisher can be provided. It should benoted that, although FIG. 2 shows that real-time ranking module 240receives an online RSS feed 250, any suitable structured representationof information can be provided. For example, a feed in Atom SyndicationFormat or some other type of weblog feed can be used to provideinformation regarding updates to a particular weblog, where theinformation also includes timestamps, author information, profileinformation, etc. In another example, a publisher can provide thecontent ranking application with access to the publisher's content foranalysis. Alternatively, a combination of RSS feeds, Atom feeds, anddata in JSON format can be provided. Again, any suitable content of anysuitable type can be ranked using real-time trends, supplemental data,and associated information.

More particularly, real-time ranking module 240 can receive a set ofcontent snippets for ranking. For example, real-time ranking module 240can receive a set of content snippets from a RSS feed (e.g., from aparticular publisher, from a particular content provider, from aparticular e-commerce website owner, from a weblog author, etc.). In amore particular example, the RSS feed can include content andinformation associated with that content (e.g., author, title, date,etc.) and, using processed trends from index 230 or any other suitablereal-time data, real-time ranking module 240 can rank the contentsnippets from the RSS feed.

Similar to the data process 210 described above, the real-time rankingmodule 240 can cleanse the received set of content snippets. Forexample, real-time ranking module 240 can analyze the received set ofcontent snippets in the RSS feed for entities, keywords, and/or anyother suitable information. Real-time ranking module 240 or any othercomponent of the application can extract entities, keywords, and/orother elements from the received set of content snippets for comparisonwith entities, keywords, and/or other elements in the trend index. Inanother example, real-time ranking module 240 can cleanse the receivedset of content snippets by removing extraneous data.

In some embodiments, real-time ranking module 240 detects similaritiesbetween each of the content snippets and the indexed trend data and userinfluence data (in index 230). For example, real-time ranking module 240can match trend data and user influence data to information obtainedfrom the content snippets and generate a score for each of the snippets.It should be noted that, in some embodiments, real-time ranking module240 runs one or more filters against the outputted scores to reducefalse positives and true negatives in the scoring.

Based on the determined similarities, a similarity score can begenerated for each of the content snippets and real-time ranking module240 can aggregate the similarity scores from matching trends. Then,based at least in part on the similarity scores, real-time rankingmodule 240 can sort the content snippets in a particular order (e.g.,descending order). An example of the aggregate similarity score isshown, for example, in FIGS. 5 and 8.

In some embodiments, an application program interface (API) forreal-time ranking module 240 is provided. The application programinterface can allow the ranked or sorted content snippets to bepublished (e.g., using a RSS feed). An example of displays illustratingthe ranking process and the published ranking of content snippets areshown in FIGS. 3-12.

Turning to FIG. 3, the content ranking application can begin byproviding a user (e.g., a publisher, a content provider, a blogger,etc.) with a display for selecting an RSS feed or any other suitablecontent feed for ranking using window 310. As noted above, the contentranking application can provide the user with a display for selectingany suitable representation of content and the content rankingapplication can provide the user with an opportunity to select frommultiple sources of content. In response to selecting the RSS feed, thecontent ranking application provides the user with a snapshot 320 of thecontent provided by the RSS feed and its associated position. Forexample, if the user accessed or visited the website corresponding tothe RSS feed, the content of that website would be provided to the userin a particular order (e.g., organized by time, organized intocategories, etc.). As shown in FIG. 3, in response to the user selectingthe RSS feed entitled “MSNBC—Headlines,” the content ranking applicationprovides the user with a preview of the unranked content and itsposition. The article entitled “Obama vs. the economy” is provided inthe first position.

It should be noted that multiple feeds can be provided to the user forselection. For example, a more detailed view of window 310 is shown inFIG. 4. As shown, the application provides the user with multiple feedsfor selection—e.g., various MSNBC feeds, various CNN feeds, various NYTimes feeds, etc.

In response to the user providing an indication to rank the selectedcontent (e.g., using “Rank Now” button 330), the content rankingapplication can retrieve, analyze, and/or apply real-time data and, inparticular trend information derived from the real-time data and/orsupplemental data, to generate the ranked listing 500 as shown in FIG.5.

As shown, in response to the user requesting to rank the “NY Times—US”feed, the content ranking application provides a ranked listing 500,where four articles have changed position from original unranked listing320. For example, the article entitled “Democrats Shy From Weiner asG.O.P. Seizes on Scandal,” based on real-time trend information and userinfluence scores, have been ranked as the top article. As noted, this isa position change of 26 places—i.e., from the 27^(th) positioned articleto the 1^(st) article. It should also be noted that the user influencescore for Representative Weiner may have been increased for a given timebased on real-time data (for example, during the collection of real-timedata and creation of the index, the application may have determined thatcontent including Representative Weiner was trending).

In addition, as also shown in FIG. 5, the content ranking applicationcan determine or calculate an aggregated trend similarity score thatencodes or takes into account the influence of the trends. For example,the aggregated trend similarity score can be calculated based at leastin part on the information relating to the trend, the content snippetthat is being rank, and the relationship and similarity between thetrend information and the content snippet. As shown, the top-mostarticle received an aggregated trend similarity score of 9.74.

In some embodiments, the content ranking application can calculate anysuitable score for the content snippet. For example, the content rankingapplication can calculate an influence score for each content snippet.It should be noted that the content ranking application can determinethe influence score in response to detecting similarities between eachof the content snippets and the indexed trend data and user influencedata.

Another example of a ranked listing is shown in FIG. 6. Similar to thelisting shown in FIG. 5, each content snippet is identified by a titleand its original position on the website. In FIG. 6, the content rankingapplication provides a description or a portion of the content in thelisting. Upon requesting to rank the received content snippets, thecontent ranking application provides the user with the ranked list shownin FIG. 6 that also includes, for each content snippet, an influencescore and an indication of the matching trends (e.g., super bowl yorkjersey).

It should be noted that, in response to selecting one of the contentsnippets from any of FIG. 5 or 6, the content ranking applicationdirects the user to the content on the website associated with thatcontent snippet. For example, as shown in FIG. 5, in response toselecting the content snippet entitled “S. Carolina Supreme Court RulesAgainst Governor,” the content ranking application links the user to thearticle published on the New York Times website. Alternatively, inresponse to selecting a content snippet, the content ranking applicationcan provide the user with an explanation for the influence score and thereal-time data that caused the change in position. For example, thecontent ranking application can provide the user with the trends in thecontent relating to the content snippet that matched trend informationfrom collected real-time data. In another example, the content rankingapplication can provide the user with options to exclude particularreal-time data (e.g., remove a particular piece of real-time data thatis skewing the results).

In some embodiments, the content ranking application includes aperformance feedback loop that provides user determined relevancefeedback from clicks. The feedback loop can contain a sample of unrankedcontent for publication to users so the engagement can be compared inrelation to the ranked content. For example, the application can, at apredetermined time, present a listing of unranked content to users andmonitor the engagement (e.g., clicks, selections, etc.). At other times,the application can provide the same users or another sample of userswith a listing of ranked content, where the content is the same as thatincluded in the listing of unranked content. The application can thencompare the engagement of the unranked content and the engagement of theranked content.

It should be noted that the engagement can be captured through a clickredirection to each of the links in the ranked or unranked contentlisting for tracking with a user identifier (userid). The clickredirection can be captured along with userid and uniform resourcelocation (URL) for each click event. Such information can be transmittedto real-time ranking module 240 (FIG. 1) to train one or more models inthe module. Accordingly, a feedback loop that provides performance dataon the real-time ranking module supports training and optimization ofthe real-time ranking module.

Referring to FIG. 7, instead of selecting a feed from window 410, thecontent ranking application provides the user with the opportunity toinput a user-defined feed. As shown in FIG. 7, the user has inputted anRSS or XML feed relating to the weblog or Internet magazine focusing onpatent and innovation news and policy named “IPWatchdog.” In response,the content ranking application uses the inputted RSS feed to retrieveunranked content and associated content information. Upon requesting torank the content from IPWatchdog, the content ranking applicationretrieves trend information from the trend index and compares the trendinformation to the unranked content and information associated with thecontent.

As shown in FIG. 8, the content ranking application has indicated thattwo articles and their subsequent user comments relating to “patenting abook” should be the highest ranked pieces of content. The user thatreceives the ranked list can, for example, choose to reorganize thewebsite to correspond to the ranked list. In another example, the usercan be provided with the opportunity to request that the content rankingapplication reorganize the website content accordingly or through an RSSfeed.

In some embodiments, the content ranking application that receives aninputted RSS feed or any other suitable form of content packets cancustomize the presentation of content for a particular browser user. Forexample, in addition to retrieving trend information from the trendindex, the content ranking application can retrieve information aboutthe particular browser user. This information can include, for example,a user profile, previous search history corresponding to the user, pastbehaviors of the user, a social graph, areas of interest, etc. Uponretrieving this additional information relating to the particularbrowser user, the content ranking application can be used to create aranked version of the website tailored to the particular browser user.That is, the content ranking application can receive content snippets ofa website and provide a customized version of the website based onreal-time data (e.g., real-time online trends, real-time influence data,etc.) and user data (e.g., user's interests, user's past behaviors,etc.).

In a more particular example, the content ranking application can beconfigured to provide a customized homepage for browser users visitingthe user's website. In this embodiment, for browser users that thecontent ranking application can obtain user information, the contentranking application can generate a customized version of the websitebased on real-time data and user data, where the customized versionappears as a homepage with links to content relevant to the particularuser. Alternatively, if user information cannot be obtained for abrowser user, that browser user is provided with unranked content or aparticular version of the website selected by the user (e.g., acontinuously updated listing of content snippets based on real-timeinfluence data).

In another more particular example, the content ranking application canbe configured to be a website that ranks third-party content based onreal-time relevance information and user information. In thisembodiment, a browser user can access the content ranking applicationand input preferences (e.g., interests, topics, demographic information,pychographic information, etc.). In response, the content rankingapplication can select one or more feeds and sort the content snippetsfrom those feeds based on real-time data and the inputted preferences.

It should be noted that the content provider, publisher, or any othersuitable entity can perform any suitable action in response to receivingthe ranked listing of content snippets. For example, the contentprovider can request that the content ranking application re-rankspecific portions of the content provider's website. In another example,a publisher can request that the content ranking application rank one ormore websites associated with the publisher.

Accordingly, the content ranking application receives content from aprovider in the form of content snippets and, using real-time trendinformation, ranks the content. This allows the provider or any othersuitable entity to discover which content is trending now, promote theappropriate content, and re-rank or redistribute content in a timelyfashion. Delivering content when and where it is relevant to theprovider's audience can result in, among other things, greatervisitation, higher engagement, and/or customer acquisition. Moreover,the content ranking application can achieve this without the need toreformat content or cache results.

In some embodiments, the content ranking application can provide theuser with an indication of the popular articles at a point in time basedon determined trend information and data feeds from one or more sources.For example, the content ranking application can provide users withcontent from various sources organized by topic (e.g., business,technology, etc.) or any other suitable categorization. As shown in FIG.9, content from multiple sources are organized into various topics andby timestamp (e.g., when the content was posted). It should be notedthat content ranking application can update the lists at a predeterminedtime (e.g., every 10 seconds, every minute, etc.). The content rankingapplication can then allow the user to rank each of the lists based onreal-time trend data and supplemental data.

In some embodiments, the content ranking application can allow the userto create a widget or portion of a website or a network that providedranked snippets of content from one or more data feeds. In a moreparticular example, a user is a real estate broker in the New York areaand has a professional weblog, page on a social networking website, or apage on the company's website that the user generally uses to post newlistings and open house information. The user also reviews recent newsand postings on a number of third-party RSS feeds and would like toshare them with the readers of the user's weblog.

As shown in FIG. 10, the content ranking application can provide theuser with a widget creation screen. In addition to inputting a name forthe widget in field 1010 and a title for the widget in field 1020, thecontent ranking application allows the user to input multiple RSS feeds.In FIG. 10, the user has inputted two RSS feeds associated with realestate websites for the New York Times—Real Estate section and Curbedinto field 1030.

In some embodiments, the widget creation screen provides the user withappearance options. For example, the user can configure the createdwidget to match the appearance settings or layout on the user's website.In another example, the user can configure the created widget to attractattention on the user's website as it contains popular content snippets.As shown, the appearance options can include width, height, backgroundcolor, font color, font size, number of results, highlighting options,hiding options, etc.

In some embodiments, the content ranking application allows the user topreview the created widget prior to placing it on the user's website.For example, as shown in FIG. 11, a preview 1100 of the created widgetis generated in response to selecting the preview button 1040 shown inFIG. 10. Preview 1100 provides the user with content snippets from theone or more inputted RSS feeds that have been sorted based on real-timedata and the score or popularity indicator used to rank the contentsnippets. For each content snippet, preview 1100 can also provide thebrowser user with an image from the content, an indication of the feedsource, a timestamp, and a trend indicator. That is, as shown in FIG.11, preview 1100 or any other suitable display presented by theapplication can display a trend indicator that informs users how aparticular piece of content is trending. This trend indicator(represented as bars in preview 1100) highlights particular pieces ofcontent, thereby distinguishing higher trending pieces of content fromother pieces of content.

As described above, preview 1100 can be configured to direct the browseruser to the content in response to selecting a particular contentsnippet.

Referring back to FIG. 10, in response to configuring the widget, thecontent ranking application allows the user to obtain the codeassociated with the widget for placement on the user's website.

FIG. 12 is a generalized schematic diagram of a system 1200 on which theinteractive inventory management application may be implemented inaccordance with some embodiments of the disclosed subject matter. Asillustrated, system 1200 may include one or more user computers 1202.User computers 1202 may be local to each other or remote from eachother. User computers 1202 are connected by one or more communicationslinks 1204 to a communications network 1206 that is linked via acommunications link 1208 to a server 1210.

System 1200 may include one or more servers 1210. Server 1210 may be anysuitable server for providing access to the application, such as aprocessor, a computer, a data processing device, or a combination ofsuch devices. For example, the application can be distributed intomultiple backend components and multiple frontend components orinterfaces. In a more particular example, backend components, such asdata collection and data distribution can be performed on one or moreservers 1210. Similarly, the graphical user interfaces displayed by theapplication, such as a data interface and an advertising networkinterface, can be distributed by one or more servers 1210 to usercomputer 1202.

More particularly, for example, each of the client 1202 and server 1210can be any of a general purpose device such as a computer or a specialpurpose device such as a client, a server, etc. Any of these general orspecial purpose devices can include any suitable components such as aprocessor (which can be a microprocessor, digital signal processor, acontroller, etc.), memory, communication interfaces, displaycontrollers, input devices, etc. For example, client 1002 can beimplemented as a personal computer, a personal data assistant (PDA), aportable email device, a multimedia terminal, a mobile telephone, aset-top box, a television, etc.

In some embodiments, any suitable computer readable media can be usedfor storing instructions for performing the processes described herein,can be used as a content distribution that stores content and a payload,etc. For example, in some embodiments, computer readable media can betransitory or non-transitory. For example, non-transitory computerreadable media can include media such as magnetic media (such as harddisks, floppy disks, etc.), optical media (such as compact discs,digital video discs, Blu-ray discs, etc.), semiconductor media (such asflash memory, electrically programmable read only memory (EPROM),electrically erasable programmable read only memory (EEPROM), etc.), anysuitable media that is not fleeting or devoid of any semblance ofpermanence during transmission, and/or any suitable tangible media. Asanother example, transitory computer readable media can include signalson networks, in wires, conductors, optical fibers, circuits, anysuitable media that is fleeting and devoid of any semblance ofpermanence during transmission, and/or any suitable intangible media.

Referring back to FIG. 12, communications network 1206 may be anysuitable computer network including the Internet, an intranet, awide-area network (“WAN”), a local-area network (“LAN”), a wirelessnetwork, a digital subscriber line (“DSL”) network, a frame relaynetwork, an asynchronous transfer mode (“ATM”) network, a virtualprivate network (“VPN”), or any combination of any of such networks.Communications links 1204 and 1208 may be any communications linkssuitable for communicating data between user computers 1202 and server1210, such as network links, dial-up links, wireless links, hard-wiredlinks, any other suitable communications links, or a combination of suchlinks. User computers 1202 enable a user to access features of theapplication. User computers 1202 may be personal computers, laptopcomputers, mainframe computers, dumb terminals, data displays, Internetbrowsers, personal digital assistants (“PDAs”), two-way pagers, wirelessterminals, portable telephones, any other suitable access device, or anycombination of such devices. User computers 1202 and server 1210 may belocated at any suitable location. In one embodiment, user computers 1202and server 1210 may be located within an organization. Alternatively,user computers 1202 and server 1210 may be distributed between multipleorganizations.

Referring back to FIG. 12, the server and one of the user computersdepicted in FIG. 12 are illustrated in more detail in FIG. 13. Referringto FIG. 13, user computer 1202 may include processor 1302, display 1304,input device 1306, and memory 1308, which may be interconnected. In apreferred embodiment, memory 1308 contains a storage device for storinga computer program for controlling processor 1302.

Processor 1302 uses the computer program to present on display 1304 theapplication and the data received through communications link 1204 andcommands and values transmitted by a user of user computer 1202. Itshould also be noted that data received through communications link 1204or any other communications links may be received from any suitablesource. Input device 1306 may be a computer keyboard, acursor-controller, dial, switchbank, lever, or any other suitable inputdevice as would be used by a designer of input systems or processcontrol systems.

Server 1210 may include processor 1320, display 1322, input device 1324,and memory 1326, which may be interconnected. In a preferred embodiment,memory 1326 contains a storage device for storing data received throughcommunications link 1208 or through other links, and also receivescommands and values transmitted by one or more users. The storage devicefurther contains a server program for controlling processor 1320.

In some embodiments, the application may include an application programinterface (not shown), or alternatively, the application may be residentin the memory of user computer 1202 or server 1210. In another suitableembodiment, the only distribution to user computer 1202 may be agraphical user interface (“GUI”) which allows a user to interact withthe application resident at, for example, server 1210.

In one particular embodiment, the application may include client-sidesoftware, hardware, or both. For example, the application may encompassone or more Web-pages or Web-page portions (e.g., via any suitableencoding, such as HyperText Markup Language (“HTML”), Dynamic HyperTextMarkup Language (“DHTML”), Extensible Markup Language (“XML”),JavaServer Pages (“JSP”), Active Server Pages (“ASP”), Cold Fusion, orany other suitable approaches).

Although the application is described herein as being implemented on auser computer and/or server, this is only illustrative. The applicationmay be implemented on any suitable platform (e.g., a personal computer(“PC”), a mainframe computer, a dumb terminal, a data display, a two-waypager, a wireless terminal, a portable telephone, a portable computer, apalmtop computer, an H/PC, an automobile PC, a laptop computer, acellular phone, a personal digital assistant (“PDA”), a combinedcellular phone and PDA, etc.) to provide such features.

It will also be understood that the detailed description herein may bepresented in terms of program procedures executed on a computer ornetwork of computers. These procedural descriptions and representationsare the means used by those skilled in the art to most effectivelyconvey the substance of their work to others skilled in the art.

A procedure is here, and generally, conceived to be a self-consistentsequence of steps leading to a desired result. These steps are thoserequiring physical manipulations of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared and otherwise manipulated. It proves convenient at times,principally for reasons of common usage, to refer to these signals asbits, values, elements, symbols, characters, terms, numbers, or thelike. It should be noted, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities.

Further, the manipulations performed are often referred to in terms,such as adding or comparing, which are commonly associated with mentaloperations performed by a human operator. No such capability of a humanoperator is necessary, or desirable in most cases, in any of theoperations described herein which form part of the present invention;the operations are machine operations. Useful machines for performingthe operation of the present invention include general purpose digitalcomputers or similar devices.

The present invention also relates to apparatus for performing theseoperations. This apparatus may be specially constructed for the requiredpurpose or it may comprise a general purpose computer as selectivelyactivated or reconfigured by a computer program stored in the computer.The procedures presented herein are not inherently related to aparticular computer or other apparatus. Various general purpose machinesmay be used with programs written in accordance with the teachingsherein, or it may prove more convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these machines will appear from the description given.

Accordingly, methods, systems, and media for content ranking usingreal-time data are provided.

It is to be understood that the invention is not limited in itsapplication to the details of construction and to the arrangements ofthe components set forth in the following description or illustrated inthe drawings. The invention is capable of other embodiments and of beingpracticed and carried out in various ways. Also, it is to be understoodthat the phraseology and terminology employed herein are for the purposeof description and should not be regarded as limiting.

Although the invention has been described and illustrated in theforegoing illustrative embodiments, it is understood that the presentdisclosure has been made only by way of example, and that numerouschanges in the details of implementation of the invention can be madewithout departing from the spirit and scope of the invention. Featuresof the disclosed embodiments can be combined and rearranged in variousways.

What is claimed is:
 1. A method for ranking content, the methodcomprising: receiving, using a processor, real-time information from aplurality of sources; supplementing the received real-time informationwith historical information and user influence information; analyzingthe supplemented real-time information from the plurality of sources todetermine real-time trend information; generating an index of thedetermined real-time trend information, wherein a plurality of keywordsare extracted from the supplemented real-time information and anassociation between the plurality of keywords is created to generate theindex; receiving a plurality of content snippets from a contentprovider; detecting similarities between each of the plurality ofcontent snippets and the determined real-time trend information usingthe generated index; calculating an influence score for each of theplurality of content snippets based on the detected similarities withthe determined real-time trend information; ranking the plurality ofcontent snippets based on the detected similarities with the determinedreal-time trend information based on the historical information, andbased on the influence score; and displaying the ranked plurality ofcontent snippets.
 2. The method of claim 1, wherein the receivedreal-time information includes at least one of structured real-timeinformation and unstructured real-time information.
 3. The method ofclaim 1, wherein the plurality of content snippets from a contentprovider comprises receiving the plurality of content snippets from adata feed.
 4. The method of claim 3, wherein the data feed is one of: aReally Simple Syndication (RSS) feed, an Atom feed, an Extensible MarkupLanguage (XML) feed, and data provided in JavaScript Object Notation(JSON) format.
 5. The method of claim 1, wherein the plurality ofsources providing real-time information comprise one or more of: asocial media network, a social messaging service, a social contentsharing service, an e-mail message, an instant message, a short messageservice (SMS) message, a web service, a weblog, and a search engine. 6.The method of claim 1, further comprising: receiving a plurality of datafeeds from a plurality of web sources; retrieving content snippets fromeach of the plurality of data feeds; ranking the retrieved contentsnippets, wherein the ranking comprises: detecting similarities betweeneach of the plurality of content snippets and real-time trendinformation from a trend index; generating a personal relevance scorefor each content snippet; and sorting each of the plurality of contentsnippets into a content listing based on the personal relevance score;and displaying the ranked content snippets.
 7. A method for rankingcontent, the method comprising: receiving, using a processor, real-timeinformation from a plurality of sources; supplementing the receivedreal-time information with historical information and user influenceinformation; analyzing the supplemented real-time information from theplurality of sources to determine real-time trend information;generating an index of the determined real-time trend information,wherein a plurality of keywords are extracted from the supplementedreal-time information and an association between the plurality ofkeywords is created to generate the index; receiving a plurality ofcontent snippets from a content provider; detecting similarities betweeneach of the plurality of content snippets and the determined real-timetrend information by extracting at least one keyword from each of theplurality of content snippets, calculating a similarity score based on acomparison of the extract keyword with each trend in the generatedindex, and aggregating the similarity score for each trend to generatean aggregated similarity score, wherein the plurality of contentsnippets are ranked based at least in part on the aggregated similarityscore; ranking the plurality of content snippets based on the detectedsimilarities; and displaying the ranked plurality of content snippets.8. A method for ranking content, the method comprising: receiving, usinga processor, real-time information from a plurality of sources;supplementing the received real-time information with historicalinformation and user influence information; analyzing the supplementedreal-time information from the plurality of sources to determinereal-time trend information; receiving a plurality of content snippetsfrom a content provider; detecting similarities between each of theplurality of content snippets and the determined real-time trendinformation; ranking the plurality of content snippets based on thedetected similarities; displaying the ranked plurality of contentsnippets; displaying a list containing unranked content snippets,wherein the content snippets are the content snippets in the rankedcontent snippets; receiving feedback from a plurality of users withrespect to the list containing the unranked content snippets; and usingthe received feedback to determine performance of one or more modelsused to rank the content snippets.
 9. A system for ranking content, thesystem comprising: a processor that: receives real-time information froma plurality of sources; supplements the received real-time informationwith historical information and user influence information; analyzes thesupplemented real-time information from the plurality of sources todetermine real-time trend information; generates an index of thedetermined real-time trend information, wherein a plurality of keywordsare extracted from the supplemented real-time information and anassociation between the plurality of keywords is created to generate theindex; receives a plurality of content snippets from a content provider;detects similarities between each of the plurality of content snippetsand the determined real-time trend information using the generatedindex; calculates an influence score for each of the plurality ofcontent snippets based on the detected similarities with the determinedreal-time trend information; ranks the plurality of content snippetsbased on the detected similarities with the determined real-time trendinformation based on the historical information, and based on theinfluence score; and displays the ranked plurality of content snippets.10. The system of claim 9, wherein the received real-time informationincludes at least one of structured real-time information andunstructured real-time information.
 11. The system of claim 9, whereinthe plurality of content snippets from a content provider comprisesreceiving the plurality of content snippets from a data feed.
 12. Thesystem of claim 11, wherein the data feed is one of: a Really SimpleSyndication (RSS) feed, an Atom feed, an Extensible Markup Language(XML) feed, and data provided in JavaScript Object Notation (JSON)format.
 13. The system of claim 9, wherein the plurality of sourcesproviding real-time information comprise one or more of: a social medianetwork, a social messaging service, a social content sharing service,an e-mail message, an instant message, a short message service (SMS)message, a web service, a weblog, and a search engine.
 14. A system forranking content, the system comprising: a processor that: receivesreal-time information from a plurality of sources; supplements thereceived real-time information with historical information and userinfluence information; analyzes the supplemented real-time informationfrom the plurality of sources to determine real-time trend information;generate an index of the determined real-time trend information, whereina plurality of keywords are extracted from the supplemented real-timeinformation and an association between the plurality of keywords iscreated to generate the index; receives a plurality of content snippetsfrom a content provider; detects similarities between each of theplurality of content snippets and the determined real-time trendinformation by extracting at least one keyword from each of theplurality of content snippets, calculating a similarity score based on acomparison of the extract keyword with each trend in the generatedindex, and aggregating the similarity score for each trend to generatean aggregated similarity score, wherein the plurality of contentsnippets are ranked based at least in part on the aggregated similarityscore; ranks the plurality of content snippets based on the detectedsimilarities; and displays the ranked plurality of content snippets. 15.The system of claim 9, wherein the processor is further configured to:receive a plurality of data feeds from a plurality of web sources;retrieve content snippets from each of the plurality of data feeds; rankthe retrieved content snippets, wherein the processor is furtherconfigured to: detect similarities between each of the plurality ofcontent snippets and real-time trend information from a trend index;generate a personal relevance score for each content snippet; and sorteach of the plurality of content snippets into a content listing basedon the personal relevance score; and display the ranked contentsnippets.
 16. A system for ranking content, the system comprising: aprocessor that: receives real-time information from a plurality ofsources; supplements the received real-time information with historicalinformation and user influence information; analyzes the supplementedreal-time information from the plurality of sources to determinereal-time trend information; receives a plurality of content snippetsfrom a content provider; detects similarities between each of theplurality of content snippets and the determined real-time trendinformation; ranks the plurality of content snippets based on thedetected similarities; displays the ranked plurality of contentsnippets; display a list containing unranked content snippets, whereinthe content snippets are the content snippets in the ranked contentsnippets; receive feedback from a plurality of users with respect to thelist containing the unranked content snippets; and use the receivedfeedback to determine performance of one or more models used to rank thecontent snippets.
 17. A non-transitory computer-readable mediumcontaining computer-executable instructions that, when executed by aprocessor, cause the processor to perform a method for ranking content,the method comprising: receiving real-time information from a pluralityof sources; supplementing the received real-time information withhistorical information and user influence information; analyzing thereal-time information from the plurality of sources to determinereal-time trend information; generating an index of the determinedreal-time trend information, wherein a plurality of keywords areextracted from the supplemented real-time information and an associationbetween the plurality of keywords is created to generate the index;receiving a plurality of content snippets from a content provider;detecting similarities between each of the plurality of content snippetsand the determined real-time trend information using the generatedindex; calculating an influence score for each of the plurality ofcontent snippets based on the detected similarities with the determinedreal-time trend information; ranking the plurality of content snippetsbased on the detected similarities, based on the historical information,and based on the influence score; and displaying the ranked plurality ofcontent snippets.
 18. A non-transitory computer-readable mediumcontaining computer-executable instructions that, when executed by aprocessor, cause the processor to perform a method for ranking content,the method comprising: receiving real-time information from a pluralityof sources; supplementing the received real-time information withhistorical information and user influence information; analyzing thesupplemented real-time information from the plurality of sources todetermine real-time trend information; generating an index of thedetermined real-time trend information, wherein a plurality of keywordsare extracted from the supplemented real-time information and anassociation between the plurality of keywords is created to generate theindex; receiving a plurality of content snippets from a contentprovider; detecting similarities between each of the plurality ofcontent snippets and the determined real-time trend information byextracting at least one keyword from each of the plurality of contentsnippets, calculating a similarity score based on a comparison of theextract keyword with each trend in the generated index, and aggregatingthe similarity score for each trend to generate an aggregated similarityscore, wherein the plurality of content snippets are ranked based atleast in part on the aggregated similarity score; ranking the pluralityof content snippets based on the detected similarities; and displayingthe ranked plurality of content snippets.
 19. A non-transitorycomputer-readable medium containing computer-executable instructionsthat, when executed by a processor, cause the processor to perform amethod for ranking content, the method comprising: receiving real-timeinformation from a plurality of sources; supplementing the receivedreal-time information with historical information and user influenceinformation; analyzing the supplemented real-time information from theplurality of sources to determine real-time trend information; receivinga plurality of content snippets from a content provider; detectingsimilarities between each of the plurality of content snippets and thedetermined real-time trend information; ranking the plurality of contentsnippets based on the detected similarities; displaying the rankedplurality of content snippets; displaying a list containing unrankedcontent snippets, wherein the content snippets are the content snippetsin the ranked content snippets; receiving feedback from a plurality ofusers with respect to the list containing the unranked content snippets;and using the received feedback to determine performance of one or moremodels used to rank the content snippets.